Distributed state estimation and fault diagnosis using reduced sensitivity to neighbor estimates with application to building control

This paper proposes solutions that reduce the inaccuracy of distributed state estimation and conse-quent performance deterioration of distributed model predictive control caused by faults and inaccurate models. A distributed state estimation method for large-scale systems is introduced. A local state estima-tion approach considers the uncertainty of neighbor estimates, which can improve the state estimation accuracy, whereas it keeps a low network communication burden. The method also incorporates the uncertainty of model parameters which improves the performance when using simplified models. The proposed method is extended with multiple models and estimates the probability of nominal and fault behavior models, which creates a distributed fault detection and diagnosis method. An example with application to the building heating control demonstrates that the proposed algorithm provides accurate state estimates to a controller and detects local or global faults while using simplified models. & COPY; 2022 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.

Automated summary

This paper proposes solutions to reduce the inaccuracy of distributed state estimation and performance deterioration of distributed model predictive control caused by faults and inaccurate models. The proposed method includes a distributed state estimation method for large-scale systems that considers the uncertainty of neighbor estimates to improve accuracy. It also incorporates the uncertainty of model parameters and extends to multiple models for creating a distributed fault detection and diagnosis method.